Digital transformation has opened up space for an environment in which the production of companies and industries can be optimized considerably. Among these technologies, one of the most useful and promising is Machine Learning. Through it, machines and software are able to become smarter, which significantly increases its efficiency.
Despite the benefits of Machine Learning, many people still consider technology complex and distant. A concept that is obviously wrong, because, in reality, it is very present in our daily lives. With that in mind, we decided to develop this content with more accurate information about Machine Learning. Continue reading and find out everything you should know about the technology and its applications!
What is Machine Learning?
At first glance, Machine Learning may appear to be a distant concept. The truth, however, is that technology is very common in our routines. Whenever a person searches a search engine, such as Google, it is precisely Machine Learning that allows the search engine to identify the best results.
Basically, Machine Learning can be described as a technology that allows the software to learn, through prescriptive data analysis and with minimal human interference. Soon, it becomes able to present high efficiency in solving specific problems.
The goal of the technology is to allow the machine to make decisions or present precise answers to a question. This result is possible because the software, which guides the hardware, has algorithms capable of analyzing data and information, identifying patterns that tend to point out the most appropriate solution to the context presented by the user.
Unlike ordinary software, which is only able to point out solutions developed in source code, a system that makes use of the concept of Machine Learning can learn while performing its function. This feature significantly increases its efficiency, making the system more useful and intelligent.
The emergence of Machine Learning
Can the machines think? Perhaps this question which was posed by the father of computing, Alan Turing, seems to be simple, but their response is capable of impacting human life in several areas. After all, thoughtful, intelligent machines have the potential to optimize human productivity, improving their standard of living.
With this issue in mind, in 1952, engineer Arthur Samuel of the Massachusetts Institute of Technology (MIT) created a program capable of learning. The first software to improve its performance through learning was a game. With each match, the program learned techniques by analyzing the game, which allowed it to create more efficient strategies.
Yes, the first software capable of learning was a little simpler, at least when compared to today’s technologies. However, it can be considered one of humanity’s greatest innovations. After all, he was rightly responsible for proving that machines were able to learn on their own.
Motivated by this advancement, engineer Arthur Samuel used, for the first time, the term Machine Learning — which to this day is adopted by enthusiasts and experts on the subject.
It is true that there is a big difference between a system that is able to learn, with data analysis and software with the ability to think on its own. Despite this fact, Machine Learning has made room for a number of innovations, which today are widely used in various areas.
Over time, the advancement of technology tends to further elevate these benefits, which makes Machine Learning one of the most important achievements of human beings!
Differences between Machine Learning and Artificial Intelligence
Although the term Artificial Intelligence has become popular — mainly thanks to the number of times it has been addressed in science fiction works such as films and books, but there is still a clearer understanding to be had for most of the population about what this technology actually is.
To be clear, we can claim that Artificial Intelligence is software capable of analyzing information, logically and reaching a series of conclusions. That’s without any human interference. So an AI is a system that thinks on its own and thus makes decisions as a human being.
Of course, this is just a simple approach. After all, an AI does not necessarily need to have logical reasoning similar to that of a human. The main point is that the system presents processing of information that can in fact be considered a “thought”.
In this concept, Machine Learning is fundamental. This is thanks to its functionality of allowing programs to learn without human aid. Therefore, it is possible to come to the conclusion that Machine Learning is one of the concepts, such as big data, that enable Artificial Intelligence.
However, it is worth mentioning that as advanced as research is in this area, none of them have yet been able to develop software that can think. Despite this, the advances already achieved have generated a number of benefits; mainly for companies, industries and research institutions.
Types of Machine Learning
When you think about all the problems a company needs to deal with, it’s easy to come to the conclusion that a single, standardized solution can’t solve them all. For this reason, different strategies and practices should be implemented by managers and their employees on a daily basis.
In a good way, a single type of Machine Learning is not able to meet all the expectations that have been placed on the technology. In the face of the situation, different types of Machine Learning have been developed, so continue reading and discover some of its main strands!
Supervised learning
In a well-managed database, all recorded information should be correct, but it is not always useful in the moment. Therefore, the ability to filter the various information and identify the ones that meet the user’s need is imperative for many software.
In order to meet this demand, supervised learning was developed. In this type of Machine Learning, the system receives a set of data and among them is the answer that the user is looking for. We can say that in supervised learning information is already available and it is up to the machine to identify and present it to the user. One of the most practical examples is searching for images in search engines such as Google.
Unsupervised learning
However, unlike an image search, in some situations there is no correct answer, or even defined patterns that indicate a possible solution. This type of event is common in performing tasks such as researching a target audience, or in studies about a company’s efficiency.
Faced with this scenario, the machine needs to analyze all available data and, through them, identify patterns that can be used as indicators. Although it is more complex, this type of Machine Learning, known as unsupervised learning, is one of the most useful because it provides the user with accurate indicators for decision making.
Semi-supervised learning
If we think about the functionalities of supervised and unsupervised learning, it is not difficult to conclude that the two have useful characteristics, aimed at solving a different type of problem.
In this context, the union of the two concepts opens space for Machine Learning capable of using a small volume of previously established information. Thus, a more complete analysis of a collection of data is analyzed in search of behaviour patterns.
This type of Machine Learning, which is named semi-supervised learning, ensures that the user lightly directs the learning of the software, increasing its efficiency.
Reinforcement learning
All the types we’ve observed so far could count on a certain number of correct information, or a heap of data to organize, in search of patterns. However, Reinforcement Learning is a type of Machine Learning that learns completely independently, this is through the evaluation of the result of its actions.
Initially, this concept may appear to be more complex, however, understanding its functioning through an example becomes a much simpler task.
Let’s say that software is tasked with performing a Day Trade operation — buying and selling shares on the same day. If it is a machine learning type by way of reinforcement, the system will learn from its own mistakes and successes, making your operation much more accurate.
In practice, we can say that this type of Machine Learning works by interacting with the environment and developing, analyzing the results obtained through this interaction.
Machine Learning applications
The various types of Machine Learning are a clear sign that technology can be used in various industries. And, in fact, the concept has been successfully applied in very different areas, which demonstrates how practical and adaptable the strategy behind it is.
Although Machine Learning is present in our daily lives, the best way to identify your applications is through examples. Continue reading and find out how the concept has made our life easier!
Search engines
Until a few years ago, when a person had to conduct research, they found themselves forced to go to a library, where they needed to flip through a series of books until they found the subject of their interest. In situations like these, there was a risk that the required book would not be available — which would cause the person to go find another library to search for the content. This was obviously not a convenient situation.
Currently, however, through search engines, any individual can have access to various contents, without having to leave the comfort of their home. Technology has reached a point where it can indicate even services, stores and businesses, also making old phone books unnecessary.
Spam detection
Email is another very useful technology. After all, through it, individuals can communicate in an agile, safe and practical way, which reflects positively even in the workplace and incorporate relations.
Unfortunately spam is a problem that reduces the security of the operation as it may contain viruses and malware. Efficient Machine Learning, however, knows how to identify messages from dubious sources and direct them to unique folders where they can’t cause problems and even block their senders so they don’t send any more suspicious messages.
Biometric recognition
At the moment, we live in a time where there are fierce disputes for were positions in the market store information, very valuable information and as a resource must be protected. This scenario then creates a demand for more modern and efficient security systems.
In this context, solutions that require biometrics or voice recognition to free up access to a particular information or area prove to be an interesting alternative. That’s because they deal with factors that are harder to circumvent. As with spam detection, Machine Learning has the potential to complement this technology, making your analysis faster and more accurate.
Automation solutions
Process robotization and automation of machinery, equipment and services present a number of benefits for companies and industries. By making use of these technologies, institutions are able to reduce their costs and increase the security of their operations, eliminating human error.
For automation to be applied, however, a solution in Machine Learning is indispensable. After all, it is through it that the machine in question can carry out its activities, becoming increasingly efficient, with the increase of its learning.
It is worth noting that automation goes far beyond the machinery park in an industry. Today, research by large companies have taken strides towards a future in which cars can move autonomously — an advancement that would be impossible without Machine Learning.
Advantages of applying Machine Learning
Now that you know how Machine Learning works and understand some of its key applications, it’s time to learn its benefits. Go ahead and carry on reading to unravel the secrets to its main benefits!
Optimising decision-making
Faced with competition from other companies and the dispute for customers and space in the market, the management team must develop efficient strategies. Without this, winning over new consumers and even maintaining the current portfolio tends to be a very complex task. For a strategy to be developed, those responsible for it need to have access to accurate information.
The company’s day-to-day operations, as well as its interaction with customers and its employees, generate a large number of information which then can be used to support the decision-making process. The problem is that the amount and the complexity of this information makes it more difficult to analyze.
In this context, Machine Learning is a very useful tool because it is able to study big data, organize your information and identify patterns. This allows the management team to understand the information they have at their disposal and thus make their best decisions.
Smarter use of features
Another determining factor for a company’s success or failure is how it allocates its resources, whether physical or financial. This is because an enterprise that mismanages its assets tends to suffer frequent losses and have low productivity rates.
Unfortunately, the task of mapping processes so that bottlenecks and production failures are identified is often slow when done manually. After all, the person responsible for it must evaluate a series of procedures, which requires a lot of time.
In addition, as dedicated and efficient as the team responsible for mapping processes is, the large number of information it needs to deal with can lead to error.
By making use of automation and machine learning solutions, the company becomes able to autonomize processes, therefore, there is an increase in the efficiency of the process, the elimination of the risk of human error and, also, the opening of space so that the management team can follow every detail of the operation, in a more precise way.
More efficient management
One of the most important activities carried out by the management team is to seek and identify investment opportunities and new business for the company. Thus, managing the company’s resources in an efficient way is critical. However, without selling your product or service, it would be hard for a business to finance its operation over time.
As difficult as it is to find good opportunities, they are always available, even in times of crisis and in this context, Machine Learning appears once again as a pertinent option.
This is because, through such technology, the team can use software that knows how to search a vast amount of data and, through analysis of the data driven culture, point out interesting opportunities for the company.
While this use of AI is recent, it has presented excellent prospects for years to come, which has excited managers and technology experts.
Examples of Machine Learning
Machine Learning is what we might call disruptive technology, because it has the potential to optimize many other technologies by creating a real revolution. Its impacts on the corporate environment have already generated a series of changes that benefit managers and entrepreneurs.
Finally, the best method to explain a concept is to apply it. Right? By taking this fact into consideration, we think it’s good to point out some of the ways machine learning has been used. Continue reading and check examples of machine learning implementation.
Database automation
No matter the size or area of activity of a company, having a data management is fundamental and the figure of an administrator for the database is of paramount importance. However, it is necessary to take into account that, no matter how engaged and competent the professional is, he is subject to making mistakes, especially when he needs to deal with a large number of tasks.
By implementing a solution in Machine Learning, the management team has the opportunity to automate the database, allowing software to perform a series of operations.
The result of this action is a reduction in the weight on the shoulders of the administrator, who starts to act more as a supervisor, verifying that the system is dealing correctly with the activities that were entrusted to it. In addition, process automation reduces the risk of human error, thus making operation safer.
Protection of systems against fraud
With the advent of the internet and technology, an increasing number of people have put aside physical money and made use of credit and debit cards. This is for both purchases made in person and for those made through electronic devices such as smartphones and computers.
Faced with this new reality, scammers began cloning cards and making fraudulent purchases, harming individuals, stores and even large financial institutions. Fortunately technology, through machine learning solutions, has evolved to the point of avoiding the vast majority of these scams, which benefits companies and society as a whole.
Efficient translation
In a globalized world, communication between people who are at different parts of the globe is often necessary. However, the difference in languages tends to be a difficult barrier to overcome.
As a solution to this problem however, machine learning solutions applied to translation allow texts and messages to be translated, in real-time, with increasing accuracy.
It is worth noting that, as the machine learns, its translation services tend to become increasingly accurate. The same technology, used to interpret language and translate texts, can be adopted in the transcription of videos and audio messages, which increases its range of possibilities.
Consequences of not investing in Machine Learning in the company
While the use of new technologies increases a company’s potential, optimizing its efficiency and consequently, productivity, it is important to note that Machine Learning takes the market to a new quality standard. For this reason, businesses that do not adapt are at serious risk of being left behind, losing their position and customers.
The study and adoption of solutions in Business Intelligence, Machine Learning, and Artificial Intelligence are vital not only for the growth of a company but also for its survival and permanence in the market. Therefore, businesses that do not invest in technology can face serious difficulties, since they will occupy a position of competitive disadvantages in the face of their adversaries.
Now that you understand how Machine Learning can make managing your business smarter, how about sharing that knowledge with your friends? Share the article on your social networks and allow them to unravel the secrets to this knowledge!
